All posts by choosehappy

Deep Learning, Digital Histology, Uncategorized

Exporting and re-importing annotations from QuPath for usage in machine learning

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Update-Nov 2020: Code has now been placed in github which enables the reading and writing of compressed geojson files at all stages of the process described below. Compression reduces the file size by approximately 93% : )

QuPath is a digital pathology tool that has become especially popular because it is both easy to use to and supports a large number of different whole slide image (WSI) file formats. QuPath is also able to perform a number of relevant analytical functions with a few mouse clicks. Of interest in this blog post is mentioning that the pathologists we tend to work with are either already familiar with QuPath, or find it easier to learn versus other tools. As a result, QuPath has become a goto tool for us for both the creation, and review of, annotations and outputs created by our algorithms.

Here we introduce a robust method using GeoJSON for exporting annotations (or cell objects) from QuPath, importing them into python as shapely objects, operating upon them, and then re-importing a modified version of them back into QuPath for downstream usage or review. As an example use case we will be looking at computationally identifying lymphocytes in WSIs of melanoma metastases using a deep learning classifier.

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Digital Histology

Computationally creating a PowerPoint presentation of experimental results using Python

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This post is an update of the previous post, which discussed how to create a powerpoint slide desk with results using Matlab. In the last couple of years, we have mostly transitioned to python for our digital pathology image analysis, in particular those tasks which employ deep learning. It thus makes sense to port our tools over as well. In this case, we’ll be looking at building powerpoint slide desks using python.

Let’s look at what we want as our final output:

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Deep Learning

Employing the albumentation library in PyTorch workflows. Bonus: Helper for selecting appropriate values!

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This brief blog post sees a modified release of the previous segmentation and classification pipelines. These versions leverage an increasingly popular augmentation library called albumentations.

ablumentation_view

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Uncategorized

Image popups on mouse over in Jupyter Notebooks

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Animation below speaks for itself : )

Finally put together a script which makes jupyter notebooks plots interactive, such that when hovering over a scatter point plot, the underlying image displays, see demo + code below:

Very useful when looking at e.g. embeddings.
If the dataset is too large to store in memory, line 70 can be replaced with a real-time load command

image_popup_on_hover

 

Code is available here: https://github.com/choosehappy/Snippets/blob/master/interactive_image_popup_on_hover.py

Digital Histology

HistoQC: An open-source quality control tool for digital pathology slides

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crack_slidepenmark_slide

airbubble_slide

Our paper is out in: Journal of Clinical Oncology: Clinical Cancer Informatics

Purpose: Digital pathology (DP), referring to the digitization of tissue slides, is beginning to change the landscape of clinical diagnostic workflows and has engendered active research within the area of computational pathology. One of the challenges in DP is the presence of artifacts and batch effects; unintentionally introduced during both routine slide preparation (e.g., staining, tissue folding, etc.) as well as digitization (e.g., blurriness, variations in contrast and hue). Manual review of glass and digital slides is laborious, qualitative, and subject to intra/inter-reader variability. There is thus a critical need for a reproducible automated approach of precisely localizing artifacts in order to identify slides which need to be reproduced or regions which should be avoided during computational analysis.

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Deep Learning

Visualizing DenseNet Using PyTorch

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Deep learning (DL) models have been performing exceptionally well on a number of challenging tasks lately. Unfortunately, given the current blackbox nature of these DL models, it is difficult to try and “understand” what the network is seeing and how it is making its decisions. Building upon our previous post discussing how to train a DenseNet for classification, we discuss here how to apply various visualization techniques to enable us to interrogate the network. The code here is designed as drop-in functionality for any network trained using the previous post, hopefully easing the burden of its implementation.

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Deep Learning, Digital Histology

Digital pathology classification using Pytorch + Densenet

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In this blog post, we discuss how to train a DenseNet style deep learning classifier, using Pytorch, for differentiating between different types of lymphoma cancer. This post and code are based on the post discussing segmentation using U-Net and is thus broken down into the same 4 components:

  1. Making training/testing databases,
  2. Training a model,
  3. Visualizing results in the validation set,
  4. Generating output.

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Deep Learning, Digital Histology

Digital Pathology Segmentation using Pytorch + Unet

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In this blog post, we discuss how to train a U-net style deep learning classifier, using Pytorch, for segmenting epithelium versus stroma regions. This post is broken down into 4 components following along other pipeline approaches we’ve discussed in the past:

  1. Making training/testing databases,
  2. Training a model,
  3. Visualizing results in the validation set,
  4. Generating output.

This model focuses on using solely Python and freely available tools (i.e., no matlab).

This blog post assumes moderate knowledge of convolutional neural networks, depending on the readers background, our JPI paper may be sufficient, or a more thorough resource such as Andrew NG’s deep learning course.

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